Using rate distortion cost as a loss function for deep learning
US11956447B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 21, 2019 |
| Grant date | Apr 9, 2024 |
| Priority date | — |
| Expiry date | Dec 21, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N19/96
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
An apparatus for encoding an image block includes a processor that presents, to a machine-learning model, the image block, obtains the partition decision for encoding the image block from the model, and encodes the image block using the partition decision. The model is trained to output a partition decision for encoding the image block by using training data for a plurality of training blocks as input, the training data including for a training block, partition decisions for encoding the training block, and, for each partition decision, a rate-distortion value resulting from encoding the training block using the partition decision. The model is trained using a loss function combining a partition loss function based upon a relationship between the partition decisions and respective predicted partitions, and a rate-distortion cost loss function based upon a relationship between the rate-distortion values and respective predicted rate-distortion values.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.